Open App
Open App

Statistics Knowledge Base

Complete learning center for statistical analysis and research methods

Start Learning
📚

Statistics Tutorials

Step-by-step guides covering fundamental and advanced statistical concepts with practical examples and applications.

🎯

Test Selection Guide

Interactive flowchart to help you choose the right statistical test based on your data type and research question.

📖

Statistics Glossary

Comprehensive dictionary of statistical terms with clear definitions and examples for easy understanding.

🔍

Research Methods Wiki

Detailed explanations of research methodologies, study designs, and best practices for data collection and analysis.

⚠️

Common Errors Guide

Learn about frequent statistical mistakes and how to avoid them in your research and data analysis projects.

🎓

APA Guidelines 2024

Current APA 7th edition guidelines for reporting statistical results in academic papers and publications.

Statistics Learning Paths

Statistics for Beginners

Start your statistical journey with fundamental concepts and basic analysis techniques.

  • Descriptive statistics basics
  • Data types and measurement scales
  • Probability fundamentals
  • Introduction to hypothesis testing

Research Methods

Learn proper research design and methodology for valid statistical analysis.

  • Experimental vs observational studies
  • Sampling techniques
  • Control and confounding variables
  • Validity and reliability

Inferential Statistics

Master hypothesis testing, confidence intervals, and statistical inference.

  • Null and alternative hypotheses
  • Type I and Type II errors
  • P-values and significance levels
  • Effect sizes and power analysis

Advanced Analytics

Explore complex statistical methods and multivariate analysis techniques.

  • Multiple regression analysis
  • ANOVA and factorial designs
  • Non-parametric methods
  • Time series and forecasting

Statistical Test Selection Flowchart

Step 1: Identify Your Data Type

Continuous: Height, weight, test scores, income

Categorical: Gender, treatment group, yes/no responses

Ordinal: Likert scales, rankings, education levels

Step 2: Determine Your Research Question

Compare Groups: t-test, ANOVA, chi-square

Examine Relationships: Correlation, regression

Describe Data: Descriptive statistics, frequencies

Step 3: Check Sample Size and Assumptions

Large Sample (n>30): Parametric tests usually appropriate

Small Sample: Check normality, consider non-parametric

Violated Assumptions: Use robust or non-parametric alternatives

Step 4: Select Appropriate Test

Two Groups: Independent t-test, Mann-Whitney U

Multiple Groups: One-way ANOVA, Kruskal-Wallis

Relationships: Pearson/Spearman correlation, regression

Statistics Glossary

P-value

The probability of obtaining results as extreme as observed, assuming the null hypothesis is true.

Confidence Interval

A range of values that likely contains the true population parameter with a specified level of confidence.

Effect Size

A measure of the magnitude of difference or relationship, independent of sample size.

Statistical Power

The probability of correctly rejecting a false null hypothesis (avoiding Type II error).

Type I Error

Incorrectly rejecting a true null hypothesis (false positive), controlled by alpha level.

Type II Error

Failing to reject a false null hypothesis (false negative), controlled by statistical power.

Central Limit Theorem

The distribution of sample means approaches normality as sample size increases, regardless of population distribution.

Degrees of Freedom

The number of independent values that can vary in a statistical calculation.

Statistics Help FAQ

Which statistical test should I use for my research?
The choice depends on your data type, sample size, and research question. Use our interactive flowchart to determine the appropriate test: t-tests for comparing means, chi-square for categorical data, ANOVA for multiple groups, and correlation for relationships.
How do I interpret p-values in statistical analysis?
P-values indicate the probability of obtaining results as extreme as observed, assuming the null hypothesis is true. Values < 0.05 are typically considered statistically significant, but interpretation should consider effect size and practical significance.
What are the most common statistical errors to avoid?
Common errors include: confusing correlation with causation, multiple testing without correction, ignoring assumptions, inappropriate test selection, and misinterpreting p-values. Our guide provides detailed explanations and prevention strategies.
How do I check statistical assumptions?
Use diagnostic plots and tests: Q-Q plots for normality, scatter plots for homoscedasticity, and formal tests like Shapiro-Wilk. Our assumption checker tool provides automated validation for common statistical tests.
What's the difference between statistical and practical significance?
Statistical significance indicates results are unlikely due to chance (p < 0.05), while practical significance considers whether the effect size is meaningful in real-world context. Both are important for interpretation.
How do I report statistical results in APA format?
Include test statistic, degrees of freedom, p-value, and effect size. Example: t(28) = 2.45, p = .021, d = 0.67. Our APA reporting guide provides templates for all common statistical tests.

Statistical Analysis Tools

APA Tables Generator

Create publication-ready statistical tables

Sample Size Calculator

Calculate required sample sizes for research

Correlation Analysis

Perform Pearson and Spearman correlation analysis

Summary Statistics

Generate descriptive statistics and data summaries

Data Visualization

Create professional statistical charts and graphs

Time Series Analysis

ARIMA modeling and forecasting tools